Method and system for computer-aided triage

ABSTRACT

A system for computer-aided triage can include a router, a remote computing system, and a client application. A method for computer-aided triage can include determining a parameter associated with a data packet, determining a treatment option based on the parameter, and transmitting information to a device associated with a second point of care.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/012,495, filed 19 Jun. 2018, which claims the benefit of U.S.Provisional Application No. 62/535,973, filed 24 Jul. 2017, U.S.Provisional Application No. 62/535,970, filed 24 Jul. 2017, and U.S.Provisional Application No. 62/521,968, filed 19 Jun. 2017, each ofwhich is incorporated in its entirety by this reference. Thisapplication is also a continuation of U.S. patent application Ser. No.16/012,458, filed 19 Jun. 2018, which claims the benefit of U.S.Provisional Application No. 62/535,973, filed 24 Jul. 2017, U.S.Provisional Application No. 62/535,970, filed 24 Jul. 2017, and U.S.Provisional Application No. 62/521,968, filed 19 Jun. 2017, each ofwhich is incorporated in its entirety by this reference.

TECHNICAL FIELD

This invention relates generally to the medical diagnostic field, andmore specifically to a new and useful system and method forcomputer-aided triage in the medical diagnostic field.

BACKGROUND

In current triaging workflows, especially those in an emergency setting,a patient presents at a first point of care, where an assessment, suchas imaging, is performed. The image data is then sent to a standardradiology workflow, which typically involves: images being uploaded to aradiologist's queue, the radiologist reviewing the images at aworkstation, the radiologist generating a report, an emergencydepartment doctor reviewing the radiologist's report, the emergencydepartment doctor determining and contact a specialist, and making adecision of how to treat and/or transfer the patient to a 2^(nd) pointof care. This workflow is typically very time-consuming, which increasesthe time it takes to treat and/or transfer a patient to a specialist. Inmany conditions, especially those involving stroke, time is extremelysensitive, as it is estimated that in the case of stroke, a patientloses about 1.9 million neurons per minute that the stroke is leftuntreated (Saver et al.). Further, as time passes, the amount and typesof treatment options, such as a mechanical thrombectomy, decrease.

Thus, there is a need in the triaging field to create an improved anduseful system and method for decreasing the time it takes to determineand initiate treatment for a patient presenting with a criticalcondition.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a schematic of a system for computer-aided triage.

FIG. 2 is a schematic of a method for computer-aided triage.

FIG. 3 depicts a variation of a method for computer-aided triage.

FIG. 4 depicts a variation of determining a patient condition during amethod for computer-aided triage.

FIGS. 5A and 5B depict a variation of an application on a user device.

FIG. 6 depict a variation of a method for computer-aided triage.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of the preferred embodiments of the inventionis not intended to limit the invention to these preferred embodiments,but rather to enable any person skilled in the art to make and use thisinvention.

1. Overview

As shown in FIG. 1, a system 100 for computer-aided triage includes arouter 110, a remote computing system 120, and a client application 130.Additionally or alternatively, the system 100 can include any number ofcomputing systems (e.g., local, remote), servers (e.g., PACS server),storage, lookup table, memory, or any other suitable components.

As shown in FIG. 2, method 200 for computer-aided triage includesdetermining a parameter associated with a data packet S220, determininga treatment option based on the parameter S230, and transmittinginformation to a device associated with a second point of care S250.Additionally or alternatively, the method 200 can include any or all of:receiving a data set at a first point of care S205, transmitting data toa remote computing system S208, preparing a data packet for analysisS210, preparing a data packet for transfer S240, aggregating data S260,or any other suitable steps performed in any suitable order.

2. Benefits

The system and method for computer-aided triage can confer severalbenefits over current systems and methods.

In some variations, the system and/or method confer the benefit ofreducing the time to match and/or transfer a patient presenting with acondition (e.g., stroke, LVO) to a specialist. In some examples, forinstance, the average time between generating a computed tomographyangiography (CTA) dataset and notifying a specialist is reduced (e.g.,from over 50 minutes to less than 8 minutes).

In some variations, the method provides a parallel process to atraditional workflow (e.g., standard radiology workflow), which canconfer the benefit of reducing the time to determine a treatment optionwhile having the outcome of the traditional workflow as a backup in thecase that an inconclusive or inaccurate determination (e.g., falsenegative, false positive, etc.) results from the method.

In some variations, the method is configured to have a high sensitivity(e.g., 87.8%, approximately 88%, between 81% and 93%, greater than 87%,etc.), which functions to detect a high number of true positive casesand help these patients reach treatment faster. In the event that thisresults in a false positive, only a minor disturbance—if any—is causedto a specialist, which affects the specialist's workflow negligibly(e.g., less than 5 minutes), if at all. Additionally or alternatively,the method can be configured to have a high specificity (e.g., 89.6%,approximately 90%, between 83% and 94%, greater than 89%, etc.), whichcan reduce a probability of determining a false negative.

In some variations, the method confers the benefit of reorganizing aqueue of patients, wherein patients having a certain condition aredetected early and prioritized (e.g., moved to the front of the queue).

In some variations, the method confers the benefit of determiningactionable analytics to optimize a workflow, such as an emergency roomtriage workflow.

Additionally or alternatively, the system and method can confer anyother benefit.

3. System

The system 100 for computer-aided triage, as shown in FIG. 1, includes arouter 110, remote computing system 120, and a client application 130.Additionally or alternatively, the system 100 can include any number ofcomputing systems (e.g., local, remote), servers (e.g., PACS server),storage, lookup table, memory, or any other suitable components.

The system 100 can implement any or all of the method 200 or any othersuitable method.

The system 100 preferably interfaces with one or more points of care(e.g., 1^(st) point of care, 2^(nd) point of care, 3^(rd) point of care,etc.), which are each typically a healthcare facility. A 1^(st) point ofcare herein refers to the healthcare facility to which a patientpresents, typically where the patient first presents (e.g., in anemergency setting). Conventionally, healthcare facilities include spokefacilities, which are often general (e.g., non-specialist, emergency,etc.) facilities, and hub (e.g., specialist) facilities, which can beequipped or better equipped (e.g., in comparison to spoke facilities)for certain procedures (e.g., mechanical thrombectomy), conditions, orpatients. Patients typically present to a spoke facility at a 1^(st)point of care, but can alternatively present to a hub facility, such aswhen it is evident what condition their symptoms reflect, when they havea prior history of a serious condition, when the condition hasprogressed to a high severity, when a hub facility is closest, randomly,or for any other reason. A healthcare facility can include any or allof: a hospital, clinic, ambulances, doctor's office, imaging center,laboratory, primary stroke center (PSC), comprehensive stroke center(CSC), stroke ready center, interventional ready center, or any othersuitable facility involved in patient care and/or diagnostic testing.

A patient can be presenting with symptoms of a condition, no symptoms(e.g., presenting for routine testing), or for any other suitablesystem. In some variations, the patient is presenting with one or morestroke symptoms (e.g., ischemic stroke symptoms), such as, but notlimited to, weakness, numbness, speech abnormalities, and facialdrooping. Typically, these patients are then treated in accordance witha stroke protocol, which typically involves an imaging protocol at animaging modality, such as, but not limited to, a non-contrast CT (NCCT)scan of the head, CTA of the head and neck, CT perfusion (CTP) of thehead.

A healthcare worker herein refers to any individual or entity associatedwith a healthcare facility, such as, but not limited to: a physician,emergency room physician (e.g., orders appropriate lab and imaging testsin accordance with a stroke protocol), radiologist (e.g., on-dutyradiologist, healthcare worker reviewing a completed imaging study,healthcare working authoring a final report, etc.), neuroradiologist,specialist (e.g., neurovascular specialist, vascular neurologist,neuro-interventional specialist, neuro-endovascular specialist,expert/specialist in a procedure such as mechanical thrombectomy,cardiac specialist, etc.), administrative assistant, healthcare facilityemployee (e.g., staff employee), emergency responder (e.g., emergencymedical technician), or any other suitable individual.

The image data can include computed tomography (CT) data (e.g.,radiographic CT, non-contrast CT, CT perfusion, etc.), preferably CTangiography (CTA) data (e.g., axial data, axial series, etc.) but canadditionally or alternatively any other suitable image data. The imagedata is preferably generated at an imaging modality (e.g., scanner atthe 1^(st) point of care), such as a CT scanner, magnetic resonanceimaging (MRI) scanner, ultrasound system, or any other scanner.Additionally or alternatively, image data can be generated from acamera, user device, accessed from a database or web-based platform,drawn, sketched, or otherwise obtained.

3.1 System—Router 110

The system 100 can include a router 110 (e.g., medical routing system),which functions to receive a data packet (e.g., dataset) includinginstances (e.g., images, scans, etc.) taken at an imaging modality(e.g., scanner) via a computing system (e.g., scanner, workstation, PACSserver) associated with a 1^(st) point of care. The instances arepreferably in the Digital Imaging and Communications in Medicine (DICOM)file format, as well as generated and transferred between computingsystem in accordance with a DICOM protocol, but can additionally oralternatively be in any suitable format. Additionally or alternatively,the instances can include any suitable medical data (e.g., diagnosticdata, patient data, patient history, patient demographic information,etc.), such as, but not limited to, PACS data, Health-Level 7 (HL7)data, electronic health record (EHR) data, or any other suitable data,and to forward the data to a remote computing system.

The instances preferably include (e.g., are tagged with) and/orassociated with a set of metadata, but can additionally or alternativelyinclude multiple sets of metadata, no metadata, extracted (e.g.,removed) metadata (e.g., for regulatory purposes, HIPAA compliance,etc.), altered (e.g., encrypted, decrypted, etc.) metadata, or any othersuitable metadata, tags, identifiers, or other suitable information.

The router 110 can refer to or include a virtual entity (e.g., virtualmachine, virtual server, etc.) and/or a physical entity (e.g., localserver). The router can be local (e.g., at a 1^(st) healthcare facility,2^(nd) healthcare facility, etc.) and associated with (e.g., connectedto) any or all of: on-site server associated with any or all of theimaging modality, the healthcare facility's PACS architecture (e.g.,server associated with physician workstations), or any other suitablelocal server or DICOM compatible device(s). Additionally oralternatively, the router can be remote (e.g., locate at a remotefacility, remote server, cloud computing system, etc.), and associatedwith any or all of: a remote server associated with the PACS system, amodality, or another DICOM compatible device such as a DICOM router.

The router 110 preferably operates on (e.g., is integrated into) asystem (e.g., computing system, workstation, server, PACS server,imaging modality, scanner, etc.) at a 1^(st) point of care butadditionally or alternatively, at a 2^(nd) point of care, remote server(e.g., physical, virtual, etc.) associated with one or both of the1^(st) point of care and the 2^(nd) point of care (e.g., PACS server,EHR server, HL7 server), a data storage system (e.g., patient records),or any other suitable system. In some variations, the system that therouter operates on is physical (e.g., physical workstation, imagingmodality, scanner, etc.) but can additionally or alternatively includevirtual components (e.g., virtual server, virtual database, cloudcomputing system, etc.).

The router 110 is preferably configured to receive data (e.g.,instances, images, study, series, etc.) from an imaging modality,preferably an imaging modality (e.g., CT scanner, MRI scanner,ultrasound machine, etc.) at a first point of care (e.g., spoke, hub,etc.) but can additionally or alternatively be at a second point of care(e.g., hub, spoke, etc.), multiple points of care, or any otherhealthcare facility. The router can be coupled in any suitable way(e.g., wired connection, wireless connection, etc.) to the imagingmodality (e.g., directly connected, indirectly connected via a PACSserver, etc.). Additionally or alternatively, the router 100 can beconnected to the healthcare facility's PACS architecture, or otherserver or DICOM-compatible device of any point of care or healthcarefacility.

In some variations, the router includes a virtual machine operating on acomputing system (e.g., computer, workstation, user device, etc.),imaging modality (e.g., scanner), server (e.g., PACS server, server at1^(st) healthcare facility, server at 2^(nd) healthcare facility, etc.),or other system. In a specific example, the router is part of a virtualmachine server. In another specific example, the router is part of alocal server.

3.2 System—Remote Computing System 120

The system 100 can include a remote computing system 120, which canfunction to receive and process data packets (e.g., dataset fromrouter), determine a treatment option (e.g., select a 2^(nd) point ofcare, select a specialist, etc.), interface with a user device (e.g.,mobile device), compress a data packet, extract and/or remove metadatafrom a data packet (e.g., to comply with a regulatory agency), orperform any other suitable function.

Preferably, part of the method 200 is performed at the remote computingsystem (e.g., cloud-based), but additionally or alternatively all of themethod 200 can be performed at the remote computing system, the method200 can be performed at any other suitable computing system(s). In somevariations, the remote computing system 120 provides an interface fortechnical support (e.g., for a client application) and/or analytics. Insome variations, the remote computing system includes storage and isconfigured to store and/or access a lookup table, wherein the lookuptable functions to determine a treatment option (e.g., 2^(nd) point ofcare), a contact associated with the 2^(nd) point of care, and/or anyother suitable information.

In some variations, the remote computing system 120 connects multiplehealthcare facilities (e.g., through a client application, through amessaging platform, etc.).

In some variations, the remote computing system 120 functions to receiveone or more inputs and/or to monitor a set of client applications (e.g.,executing on user devices, executing on workstations, etc.).

3.3 System—Application 130

The system 100 can include one or more applications 130 (e.g., clients,client applications, client application executing on a device, etc.),such as the application shown in FIGS. 5A and 5B, which individually orcollectively function to provide one or more outputs (e.g., from theremote computing system) to a contact. Additionally or alternatively,they can individually or collectively function to receive one or moreinputs from a contact, provide one or more outputs to a healthcarefacility (e.g., first point of care, second point of care, etc.),establish communication between healthcare facilities, or perform anyother suitable function.

In some variations, one or more features of the application (e.g.,appearance, information content, information displayed, user interface,graphical user interface, etc.) are determined based on any or all of:what kind of device the application is operating on (e.g., user devicevs. healthcare facility device, mobile device vs. stationary device),where the device is located (e.g., 1^(st) point of care, 2^(nd) point ofcare, etc.), who is interacting with the application (e.g., useridentifier, user security clearance, user permission, etc.), or anyother characteristic. In some variations, for instance, an applicationexecuting on a healthcare facility will display a 1^(st) set ofinformation (e.g., uncompressed images, metadata, etc.) while anapplication executing on a user device will display a 2^(nd) set ofinformation (e.g., compressed images, no metadata, etc.). In somevariations, the type of data to display is determined based on any orall of: an application identifier, mobile device identifier, workstationidentifier, or any other suitable identifier.

The outputs of the application can include any or all of: an alert ornotification (e.g., push notification, text message, call, email, etc.),an image set, a set of tools for interacting with the image set (e.g.,panning, zooming, rotating, window leveling, scrolling, maximumintensity projection, changing orientation of 3D scan, etc.), amessaging platform (e.g., text, video, etc.), a telecommunicationplatform, directory of contact information (e.g., 1^(st) point of carecontact info, 2^(nd) point of care contact info, etc.), tracking of aworkflow or activity (e.g., real-time or near real-time updates ofpatient status/workflow/etc.), analytics based on or related to thetracking (e.g., predictive analytics such as predicted time remaining inradiology workflow or predicted time until stroke reaches a certainseverity, average time in a workflow, average time to transition to asecond point of care, etc.), or any other suitable output.

The inputs can include any or all of the outputs described previously,touch inputs (e.g., received at a touch-sensitive surface), audioinputs, optical inputs, or any other suitable input. The set of inputspreferably includes an input indicating receipt of an output by acontact. This can include an active input from the contact (e.g.,contact makes selection at application), a passive input (e.g., readreceipt), or any other input.

In one variation, the system 100 includes a mobile device application130 and a workstation application 130—both connected to the remotecomputing system—wherein a shared user identifier (e.g., specialistaccount, user account, etc.) can be used to connect the applications(e.g., retrieve a case, image set, etc.) and determine the informationto be displayed at each application (e.g., variations of imagedatasets). In one example, the information to be displayed (e.g.,compressed images, high-resolution images, etc.) can be determined basedon: the system type (e.g., mobile device, workstation), the applicationtype (e.g., mobile device application, workstation application,), theuser account (e.g., permissions, etc.), any other suitable information,or otherwise determined.

The application can include any suitable algorithms or processes foranalysis, and part or all of the method 200 can be performed by aprocessor associated with the application.

The application preferably includes both front-end (e.g., applicationexecuting on a user device, application executing on a workstation,etc.) and back-end components (e.g., software, processing at a remotecomputing system, etc.), but can additionally or alternatively includejust front-end or back-end components, or any number of componentsimplemented at any suitable system(s).

3.4 System—Additional Components

The system 100 and/or or any component of the system 100 can optionallyinclude or be coupled to any suitable component for operation, such as,but not limited to: a processing module (e.g., processor,microprocessor, etc.), control module (e.g., Controller,Microcontroller), Power Module (e.g., Power Source, Battery,Rechargeable battery, mains power, inductive charger, etc.), sensorsystem (e.g., optical sensor, camera, microphone, motion sensor,location sensor, etc.), or any other suitable component.

3.5 System—Variations

In one variation, the system includes a router 110, which operates at acomputing system at a 1^(st) point of care and receives image data froman imaging modality. The router transmits the image data to a remotecomputing system, wherein a series of algorithms (e.g., machine learningalgorithms) are performed at the remote computing system, whichdetermines a hypothesis for whether or not a suspected condition ispresent based on the image data and/or any associated metadata. Based onthe determination, a contact is determined from a lookup table (e.g., instorage at the remote computing system), wherein the contact is notifiedat a user device (e.g., personal device) and sent image data through aclient application executing on the user device. One or more inputs fromthe contact at the application can be received at the remote computingsystem, which can be used to determine a next point of care for thepatient.

4. Method

As shown in FIG. 2, the method 200 includes determining a parameterassociated with a data packet S220, determining a treatment option basedon the parameter S230, and transmitting information to a deviceassociated with a second point of care S250. Additionally oralternatively, the method 200 can include any or all of: receiving adata set at a first point of care S205, transmitting data to a remotecomputing system S208, preparing a data packet for analysis S210,preparing a data packet for transfer S240, aggregating data S260, or anyother suitable steps performed in any suitable order.

The method 200 is preferably performed separate from but in parallelwith (e.g., contemporaneously with, concurrently with, etc.) a standardradiology workflow (e.g., as shown in FIG. 3), but can additionally oralternatively be implemented within a standard workflow, be performed ata separate time with respect to a standard workflow, or be performed atany suitable time.

The method 200 can be partially or fully implemented with the system 100or with any other suitable system.

The method 200 functions to improve communication across healthcarefacility networks (e.g., stroke networks, spokes and hubs, etc.) anddecrease the time required to transfer a patient having a suspectedtime-sensitive condition (e.g., brain condition, stroke, ischemicstroke, large vessel occlusion (LVO), cardiac event, trauma, etc.) froma first point of care (e.g., spoke, non-specialist facility, strokecenter, ambulance, etc.) to a second point of care (e.g., hub,specialist facility, comprehensive stroke center, etc.), wherein thesecond point of care refers to a healthcare facility equipped to treatthe patient. In some variations, the second point of care is the firstpoint of care, wherein the patient is treated at the healthcare facilityto which he or she initially presents.

The method 200 preferably functions as a parallel workflow tool, whereinthe parallel workflow is performed contemporaneously with (e.g.,concurrently, during, partially during) a standard radiology workflow(e.g., radiologist queue), but can additionally or alternatively beimplemented within a standard workflow (e.g., to automate part of astandard workflow process, decrease the time required to perform astandard workflow process, etc.), be performed during a workflow otherthan a radiology workflow (e.g., during a routine examination workflow),or at any other suitable time.

The method 200 is preferably performed in response to a patientpresenting at a first point of care. The first point of care can be anemergency setting (e.g., emergency room, ambulance, imaging center,etc.) or any suitable healthcare facility, such as those describedpreviously. The patient is typically presenting with (or suspected to bepresenting with) a time-sensitive condition, such as a neurovascularcondition (e.g., stroke, ischemic stroke, occlusion, large vesselocclusion (LVO), thrombus, aneurysm, etc.), cardiac event or condition(e.g., cardiovascular condition, heart attack, etc.), trauma (e.g.,acute trauma, blood loss, etc.), or any other time-sensitive (e.g.,life-threatening) condition. In other variations, the method isperformed for a patient presenting to a routine healthcare setting(e.g., non-emergency setting, clinic, imaging center, etc.), such as forroutine testing, screening, diagnostics, imaging, clinic review,laboratory testing (e.g., blood tests), or for any other reason.

Any or all of the method can be performed using any number of deeplearning (e.g., machine learning) modules. Each module can utilize oneor more of: supervised learning (e.g., using logistic regression, usingback propagation neural networks, using random forests, decision trees,etc.), unsupervised learning (e.g., using an Apriori algorithm, usingK-means clustering), semi-supervised learning, reinforcement learning(e.g., using a Q-learning algorithm, using temporal differencelearning), and any other suitable learning style. Each module of theplurality can implement any one or more of: a regression algorithm(e.g., ordinary least squares, logistic regression, stepwise regression,multivariate adaptive regression splines, locally estimated scatterplotsmoothing, etc.), an instance-based method (e.g., k-nearest neighbor,learning vector quantization, self-organizing map, etc.), aregularization method (e.g., ridge regression, least absolute shrinkageand selection operator, elastic net, etc.), a decision tree learningmethod (e.g., classification and regression tree, iterative dichotomiser3, C4.5, chi-squared automatic interaction detection, decision stump,random forest, multivariate adaptive regression splines, gradientboosting machines, etc.), a Bayesian method (e.g., naïve Bayes, averagedone-dependence estimators, Bayesian belief network, etc.), a kernelmethod (e.g., a support vector machine, a radial basis function, alinear discriminate analysis, etc.), a clustering method (e.g., k-meansclustering, expectation maximization, etc.), an associated rule learningalgorithm (e.g., an Apriori algorithm, an Eclat algorithm, etc.), anartificial neural network model (e.g., a Perceptron method, aback-propagation method, a Hopfield network method, a self-organizingmap method, a learning vector quantization method, etc.), a deeplearning algorithm (e.g., a restricted Boltzmann machine, a deep beliefnetwork method, a convolution network method, a stacked auto-encodermethod, etc.), a dimensionality reduction method (e.g., principalcomponent analysis, partial lest squares regression, Sammon mapping,multidimensional scaling, projection pursuit, etc.), an ensemble method(e.g., boosting, boostrapped aggregation, AdaBoost, stackedgeneralization, gradient boosting machine method, random forest method,etc.), and any suitable form of machine learning algorithm. Each modulecan additionally or alternatively be a: probabilistic module, heuristicmodule, deterministic module, or be any other suitable module leveragingany other suitable computation method, machine learning method, orcombination thereof.

Each module can be validated, verified, reinforced, calibrated, orotherwise updated based on newly received, up-to-date measurements; pastmeasurements recorded during the operating session; historicmeasurements recorded during past operating sessions; or be updatedbased on any other suitable data. Each module can be run or updated:once; at a predetermined frequency; every time the method is performed;every time an unanticipated measurement value is received; or at anyother suitable frequency. The set of modules can be run or updatedconcurrently with one or more other modules, serially, at varyingfrequencies, or at any other suitable time. Each module can bevalidated, verified, reinforced, calibrated, or otherwise updated basedon newly received, up-to-date data; past data or be updated based on anyother suitable data. Each module can be run or updated: in response todetermination of an actual result differing from an expected result; orat any other suitable frequency.

4.1 Method—Receiving Data from a First Point of Care S205

The method 200 can include receiving data (e.g., data packet) from afirst point of care S205, which functions to collect data relevant toassessing a patient condition.

The data is preferably received at a router 110, wherein the router isin the form of a virtual machine operating on a computing system (e.g.,computer, workstation, quality assurance (QA) workstation, readingworkstation, PACS server, etc.) coupled to or part of an imagingmodality (e.g., CT scanner, MRI scanner, etc.), or any other suitablerouter. Additionally or alternatively, data can be received at a remotecomputing system (e.g., from an imaging modality, from a database, aserver such as a PACS server, an internet search, social media, etc.),or at any other suitable computing system (e.g., server) or storage site(e.g., database). In some variations, for instance, a subset of the data(e.g., image data) is received at the router while another subset of thedata (e.g., patient information, patient history, etc.) is received at aremote computing system. In a specific example, the data subset receivedat the router is eventually transmitted to the remote computing systemfor analysis.

The first point of care is often a spoke facility (e.g., non-specialistfacility) but can alternatively be a hub facility (e.g., specialistfacility), mobile facility or transportation (e.g., ambulance), or anyother suitable healthcare facility.

S205 is preferably performed in response to (e.g., after, in real timewith, substantially in real time with, with a predetermined delay, witha delay of less than 10 seconds, with a delay of less than 1 minute, atthe prompting of a medical professional, etc.) the data (e.g., each of aset of instances) being generated at the imaging modality. Additionallyor alternatively, S205 can be performed in response to a set of multipleinstances being generated by the imaging modality (e.g., after a partialseries has been generated, after a full series has been generated, aftera study has been generated, etc.), in response to a metadata tag beinggenerated (e.g., for an instance, for a series, for a study, etc.), inresponse to a trigger (e.g., request for images), throughout the method(e.g., as a patient's medical records are accessed, as information isentered a server, as information is retrieved from a server, etc.), orat any other suitable time.

S205 can be performed a single time or multiple times (e.g.,sequentially, at different times in the method, once patient conditionhas progressed, etc.). In one variations, each instance is received(e.g., at a router, at a remote computing system, etc.) individually asit is generated. In a second variation, a set of multiple instances(e.g., multiple images, full series, etc.) are received together (e.g.,after a scan has completed, after a particular anatomical component hasbeen imaged, etc.).

The router (e.g., virtual machine, virtual server, application runningon the image sampling system or a computing system connected to theimage sampling system, etc.) can be continuously ‘listening’ (e.g.,operating in a scanning mode, receiving mode, coupled to or include aradio operating in a suitable mode, etc.) for information from theimaging modality, can receive information in response to prompting of ahealthcare facility worker, in response to a particular scan type beinginitiated (e.g., in response to a head CTA scan being initiated), or inresponse to any other suitable trigger.

Image data is preferably received at the router (e.g., directly,indirectly, etc.) from the imaging modality (e.g., scanner) at which thedata was generated. Additionally or alternatively, image data or anyother data can be received from any computing system associated with thehealthcare facility's PACS server, any DICOM-compatible devices such asa DICOM router, or any other suitable computing system. The image datais preferably in the DICOM format but can additionally or alternativelyinclude any other data format.

In addition to or alternative to image data, the data can include blooddata, electronic medical record (EMR) data, unstructured EMR data,health level 7 (HL7) data, HL7 messages, clinical notes, or any othersuitable data related to a patient's medical state, condition, ormedical history.

The data preferably includes a set of one or more instances (e.g.,images), which can be unorganized, organized (e.g., into a series, intoa study, a sequential set of instances based on instance creation time,acquisition time, image position, instance number, unique identification(UID), other acquisition parameters or metadata tags, anatomical featureor location within body, etc.), complete, incomplete, randomly arranged,or otherwise arranged.

Each instance preferably includes (e.g., is tagged with) a set ofmetadata associated with the image dataset, such as, but not limited to:one or more patient identifiers (e.g., name, identification number, UID,etc.), patient demographic information (e.g., age, race, sex, etc.),reason for presentation (e.g. presenting symptoms, medical severityscore, etc.), patient history (e.g., prior scans, prior diagnosis, priormedical encounters, etc.), medical record (e.g. history of presentillness, past medical history, allergies, medications, family history,social history, etc.), scan information, scan time, scan type (e.g.,anatomical region being scanned, scanning modality, scanner identifier,etc.), number of images in scan, parameters related to scan acquisition(e.g., timestamps, dosage, gurney position, scanning protocol, contrastbolus protocol, etc.), or any other suitable information.

In some variations, any or all of the data (e.g., image data) is taggedwith metadata associated with the standard DICOM protocol.

In some variations, one or more tags is generated and/or applied to thedata after the data is generated at an imaging modality. In someexamples, the tag is an identifier associated with the 1^(st) point ofcare (e.g., 1^(st) point of care location, imaging modality identifier,etc.), which can be retrieved by a 2^(nd) point of care in order tolocate the patient (e.g., to enable a quick transfer of the patient, toinform a specialist of who to contact or where to reach the patient,etc.).

Additionally or alternatively, image data can be received withoutassociated metadata (e.g., metadata identified later in the method,dataset privately tagged later with metadata later in the method, etc.)

Data can be received (e.g., at the router) through a wired connection(e.g., local area network (LAN) connection), wireless connection, orthrough any combination of connections and information pathways.

In some variations, images are generated at an imaging modality (e.g.,CT scanner) in response to a standard stroke protocol.

4.2 Method—Transmitting Data to a Remote Computing System S208

The method can include transmitting data to remote computing system(e.g., remote server, PACS server, etc.) S208, which functions to enableremote processing of the data, robust process, or fast processing (e.g.,faster than analysis done in clinical workflow, faster than done in astandard radiology workflow, processing less than 20 minutes, less than10 minutes, less than 7 minutes, etc.) of the dataset.

The data (e.g., image data, image data and metadata, etc.) is preferablytransmitted to a remote computing system from a router (e.g., virtualmachine operating on a scanner) connected to a computing system (e.g.,scanner, workstation, PACS server, etc.) associated with a healthcarefacility, further preferably where the patient first presents (e.g.,1^(st) point of care), but can additionally or alternatively betransmitted from any healthcare facility, computing system, or storagesite (e.g., database).

Each instance (e.g., image) of the dataset (e.g., image dataset) ispreferably sent individually as it is generated at an imaging modalityand/or received at a router, but additionally or alternatively, multipleinstances can be sent together after a predetermined set (e.g., series,study, etc.) has been generated, after a predetermined interval of timehas passed (e.g., instances sent every 10 seconds), upon the promptingof a medical professional, or at any other suitable time. Furtheradditionally or alternatively, the order in which instances are sent toa remote computing system can depend one or more properties of thoseinstances (e.g., metadata). S208 can be performed a single time ormultiple times (e.g., after each instance is generated).

S208 can include transmitting all of the dataset (e.g., image datasetand metadata), a portion of the dataset (e.g., only image dataset,subset of image dataset and metadata, etc.), or any other information oradditional information (e.g., supplementary information such assupplementary user information).

The data is preferably transmitted through a secure channel, furtherpreferably through a channel providing error correction (e.g., overTCP/IP stack of 1^(st) point of care), but can alternatively be sentthrough any suitable channel.

S208 can include any number of suitable sub-steps performed prior to orduring the transmitting of data to the remote computing system. Thesesub-steps can include any or all of: encrypting any or all of thedataset (e.g., patient information) prior to transmitting to the remotecomputing system, removing information (e.g., sensitive information),supplementing the dataset with additional information (e.g.,Supplementary patient information, supplemental series of a study,etc.), compressing any or all of the dataset, or performing any othersuitable process.

4.3 Method—Preparing a Data Packet for Analysis S210

The method 200 preferably includes preparing a data packet S210 foranalysis, which can function to decrease the time required to analyzethe data packet, eliminate irrelevant data packets from furtheranalysis, remove irrelevant data from a data packet (e.g., irrelevantanatomical regions), or perform any other suitable function.

Preparing a data packet for analysis can include organizing a set ofinstances (e.g., images, slices, scans, etc.), preferably into a series,but additionally or alternatively into a study, or any other suitablegrouping of images. The organization is preferably performed in responseto generating a set of instances (e.g., at an imaging modality), but canadditionally or alternatively be performed in response to receiving aset of instances at a location (e.g., router, remote computing system,server such as a PACS server, etc.), at the request of an individual(e.g., healthcare worker), in response to a trigger, or at any othersuitable time. Additionally or alternatively, the set of instances canbe performed multiple times throughout the method (e.g., based on thesame organization scheme/metadata, based on different organizationschemes/metadata, etc.). The organization can be done at a remotecomputing system, a healthcare facility computing system, a virtualmachine (e.g., operating on a healthcare facility computing system), orat any other suitable computing or processing system, physical orvirtual, local (e.g., at a healthcare facility) or remote. The set ofimages are preferably organized based on a set of metadata (e.g.,metadata tags, conventional DICOM metadata tags, etc.), but canadditionally or alternatively be organized in any other suitable way(e.g., organized by time of receipt, ranked order of importance, etc.).In one variation, a set of images are organized into a series based on aset of metadata, wherein the series is formed from images havingmetadata corresponding to any or all of the following: images taken inan axial series, images each corresponding to a thin slice (e.g., 0.625millimeters (mm) or thinner), no missing slices (e.g., no jump in aslice number between adjacent images), a consistent pixel spacing acrossthe series, and aligned instance numbers and positions. Additionally oralternatively, any other metadata can be used to determine the series.

In some variations, the method includes excluding a data packet (e.g.,set of instances) from further processing if one or more of a set ofmetadata are not satisfied, such as, but not limited to, the metadatalisted above.

Preparing a data packet can additionally or alternatively includeextraction of data, such as one or more materials or features in the setof instances (e.g., series), which can function to reduce thecomputational cost and time of one or more remaining steps of the method(e.g., by removing irrelevant features in one or more instances). Thiscan include any or all of pixel-based methods, voxel-based methods,comparing non-image data (e.g., blood test results) with one or morepredetermined thresholds, or any other suitable method(s). The data ispreferably extracted after the data packet has been organized (e.g.,into a series), but can additionally or alternatively be performed inabsence of the data packet being organized, in response to a trigger, inmultiple steps and/or at multiple times throughout the method (e.g.,extract a first material and then a subset of that material), or at anyother point during the method. The data is preferably extracted at aremote computing system (e.g., single computing system), but canadditionally or alternatively be performed at any suitable computingsystem. Data can be extracted based on any or all of: HU valuethresholding, photomasks, dilation, erosion, or any other technique.

In some variations, such as those involving patients presenting with astroke symptom or condition (e.g., large vessel occlusion), this caninclude extracting portions of the image corresponding to soft matter(e.g., brain tissue, cerebral fluid, blood, etc.) and/or removingportions of the image correspond to hard matter (e.g., bone, skull,etc.). This is preferably done by leveraging the fact that soft mattercorresponds to a set of low Hounsfield Unit (HU) values, which isdifferentiated from any surrounding hard matter (e.g., bone, skull),which corresponds to a set of high HU values.

In a specific example, a bone mask is determined and defined as a set ofvoxels having an HU value above a predetermined threshold (e.g., 750 HU,700 HU, 800 HU, between 600 HU and 900 HU, etc.). The bone mask is thendilated with a series of kernels of increasing size until it completelyencloses a set of voxels of low HU values (e.g., less than thepredetermined threshold), thereby defining a soft matter mask. The softmatter mask is dilated to compensate for the dilation of the bone mask.If the process of defining the soft matter mask fails, this can indicatethat the skull has undergone a craniotomy, which in some cases can beused in determining a diagnosis, informing a contact or second point ofcare, or in any other point in the method. Once the soft matter mask isdilated, the mask can then be applied to the set of instances (e.g.,organized set of instances, series, etc.), and the HU value of voxelsoutside of the mask is set to zero.

Preparing a data packet preferably includes evaluating one or moreexclusion criteria in the set of instances, which can function to verifythat a set of instances is relevant for evaluation in the rest of themethod, save time and/or resources by eliminating irrelevant sets ofinstances, route a set of instances corresponding to one or moreexclusion criteria to another workflow in a healthcare facility, orperform any other suitable function. Alternatively, the method canpartially or fully process all sets of instances. The exclusion criteriaare preferably applied after data has been extracted (e.g., to reduceprocessing time), but can additionally or alternatively be performedprior to or in absence of the extraction of data, multiple timesthroughout the method (e.g., different exclusion criteria applieddepending on the degree of processing of the set of instances), or atany other suitable time during the method. Evaluating data for exclusioncriteria is preferably performed at the remote computing system, but canadditionally or alternatively be performed at any other computingsystem.

The exclusion criteria preferably include any or all of: the presence ofan artifact in one or more of the set of instances (e.g., metallicartifact, aneurysm clip, etc.), improper timing at which the set ofinstances were taken at an imaging modality (e.g., premature timing,improper timing of a bolus, etc.), one or more incomplete regions (e.g.,features, anatomical features, etc.) in the set of instances (e.g.,incomplete skull, incomplete vessel, incomplete soft matter region,etc.), an incorrect scan type or body part (e.g., non-head CT scan,non-contrast CT scan, etc.), poor image quality (e.g., blurry images,low contrast, etc.), movement of patient during scan, or any othersuitable exclusion criteria.

In one variation, a set of instances (e.g., images, series, etc.) areevaluated to determine if an artifact is present, wherein the set ofinstances is excluded from further steps in the method if an artifact isfound. In a specific example, the method includes inspecting the HUvalues of voxels in a soft matter mask, wherein voxels having a valueabove a predetermined threshold (e.g., 3000 HU, between 2000 and 4000HU, etc.) are determined to be a metallic artifact (e.g., aneurysmclip).

In a second variation, a set of instances are evaluated to determine ifbad bolus timing occurred during the generation of the set of instancesat an imaging modality. In a specific example, a soft matter mask iseroded with a wide kernel, which functions to remove potential high HUvoxels due to a partial volume effect caused by bone voxels. The HUvalues of the voxels within the eroded mask are inspected and the numberof voxels having a value above a predetermined threshold (e.g., 100 HU,between 10 and 200 HU, etc.) can be counted (e.g., correlated to avolume) and used to determine if the timing of the scan was premature.If the timing of the scan was premature, the contrast (e.g., contrastagent, dye, etc.) in a contrast CT scan, for instance, should not bevisible within the soft matter and typical HU values of the voxels willbe below the predetermined threshold (e.g., less than 100 HU). If thetotal volume of voxels having a value greater than the predeterminedthreshold is less than a predetermined volume threshold (e.g., 10 cc, 20cc, 5 cc, etc.), the set of instances (e.g., series) can be rejectedbased on bad bolus timing (e.g., premature scan). In some specificexamples, this process is selectively applied (e.g., only to an anteriorpart of the soft matter to avoid mistaking of calcifications of thechoroid plexus or pineal gland as contrast).

In a third variation, a set of instances are evaluated to determine ifan anatomical feature is incomplete or missing from the set ofinstances. In a specific example, the set of instances are evaluated todetermine if a complete or nearly complete (e.g., area or volume above apredetermined threshold) skull is present. This can include inspecting atotal area of cerebral soft matter in a particular slice (e.g., topslice), wherein if the total area exceeds a predetermined threshold(e.g., 80 centimeters squared, 90 centimeters squared, between 70 and100 centimeters squared, etc.), the set of instances is excluded asbeing incomplete.

S210 can include one or more registration steps (e.g., imageregistration steps), wherein any or all of the set of instances (e.g.,soft matter extracted from set of instances) are registered to areference set of instances (e.g., reference series), which can functionto align, scale, calibrate, or otherwise adjust the set of instances.The registration step is preferably performed in response to a datapacket being filtered through a set of exclusion criteria, but canadditionally or alternatively be performed prior to or in absence of thefiltering, multiple times throughout the method, or at any othersuitable time.

The registration step can be intensity-based, feature-based, or anyother type or registration and can include any or all of point-mapping,feature-mapping, or any other suitable process. The reference set ofinstances is preferably determined from a training set but canalternatively be determined from any suitable dataset,computer-generated, or otherwise determined. The reference series can beselected for any or all of orientation, size, three-dimensionalpositioning, clarity, contrast, or any other feature. In one variation,a references series is selected from a training set, wherein thereference series is selected based on a feature parameter (e.g., largestfeature size such as largest skull, smallest feature size, etc.) and/ora degree of alignment (e.g., maximal alignment, alignment above apredetermined threshold, etc.). Additionally or alternatively, any othercriteria can be used to determine the reference series, the referenceseries can be randomly selected, formed from aggregating multiple setsof instances (e.g., multiple patient series), or determined in any othersuitable way. In some variations, the registration is performed with oneor more particular software packages (e.g., SimpleElastix). In aspecific example, the registration is performed through afflineregistration (e.g., in SimpleElastix) with a set of predetermined (e.g.,default) parameters and iterations (e.g., 4096 iterations, between 3000and 5000 iterations, above 1000 iterations, above 100 iterations, etc.)in each registration step.

In one variation, a skull-stripped series (e.g., series having softmatter extracted) is registered to a reference series chosen from atraining set, wherein the reference series was chosen based on it havinga large skull size and a high level of alignment among its set ofinstances.

In a second variation, a skull-stripped series is registered to areference series formed from an aggregated set of series.

Additionally or alternatively, preparing the data packet can include anyother suitable steps.

4.4 Method—Determining a Parameter Associated with the Data Packet S220

The method 200 preferably includes determining a parameter associatedwith the data packet S220 (e.g., as shown in FIG. 4), which functions toassess a patient condition which subsequently informs the rest of themethod 200. Additionally or alternatively, S220 can function to reducethe time to transfer a patient to a second point of care, haltprogression of the condition, or perform any other suitable function.S220 is preferably fully performed at a remote computing system (e.g.,remote server, cloud-based server, etc.), further preferably a remotecomputing system having a graphics processing unit (GPU), but canadditionally or alternatively be partially performed at any suitableremote computing system, be partially or fully performed at a localcomputing system (e.g., workstation), server (e.g., PACS server), at aprocessor of a user device, or at any other system. S220 is preferablypartially or fully performed using software including one or morealgorithms, further preferably one or more multi-step algorithmscontaining steps that are either trained (e.g., trained through machinelearning, trained through deep learning, continuously trained, etc.) ornon-trained (e.g., rule-based image processing algorithms orheuristics). Additionally or alternatively, any software can beimplemented.

S220 preferably includes identifying (e.g., locate, isolate, measure,quantify, etc.) an anatomical feature S222 within the data packet,further preferably within a registered series of images butalternatively within any suitable image dataset. This can be performedthrough any number of computer vision techniques, such as objectrecognition, object identification, object detection, or any other formof image analysis. In some variations, the anatomical feature analysisis performed at least partially through image segmentation, wherein thesegmentation includes any or all of: thresholding, clustering methods,dual clustering methods, compression-based methods, histogram-basedmethods, region-growing methods, partial differential equation-basedmethods, variational methods, graph partitioning methods, watershedtransformations, model based segmentation, multi-scale segmentation,semi-automatic segmentation, trainable segmentation, or any suitableform of segmentation. The method can additionally or alternativelyinclude any number of segmentation post-processing steps, such asthresholding, connectivity analyses, or any other processing. Thesegmentation is preferably performed with a convolutional neural network(CNN), further preferably feed-forward deep CNN (e.g., usingthree-dimensional convolutions, two-dimensional convolutions, etc.), butcan additionally or alternatively be performed using any suitablealgorithm or process.

In variations, such as those involving stroke (e.g., ischemic stroke,LVO, etc.), the anatomical feature can be one or more blood vessels(e.g., arteries, large paired arteries, etc.), such as the internalcarotid artery (ICA) (e.g., terminal ICA (t-ICA)), middle cerebralartery (MCA), or any other vessel or other anatomical feature.Additionally or alternatively, the anatomical feature can be soft matter(e.g., brain tissue), hard matter (e.g., bone), or any other feature. Inother variations, the anatomical feature can be a part of the heart(e.g., vessel, artery, lobe, etc.), a bone (e.g., fractured bone), orany other part of the body.

S222 is preferably performed after the image data (e.g., series) hasbeen registered to a reference series but can additionally oralternatively be performed prior to or in absence of a registrationstep, in response to a trigger, multiple times throughout the method, orat any other suitable time.

In some variations, such as in the case of a patient presenting with astroke (e.g., ischemic stroke, vessel occlusion, LVO, etc.), a largevessel region (e.g., t-ICA and MCA-M1 segments) is segmented.

In other variations, an anatomical feature (e.g., thrombus, aneurysm,etc.) within a vessel is identified. In a specific example, forinstance, a clot is segmented. The segmented clot can be assessed (e.g.,using other processes of the method 200) to determine, for instance, oneor more parameters (e.g., size, length, volume, etc.) of the clot andcompare the one or more parameters with one or more predeterminedthresholds (e.g., anatomical thresholds or parameters).

In yet other variations, an anatomical feature outside of the brainvasculature, such as a tumor, tissue region (e.g., infarcted tissue),swelled region, or any other suitable feature can be identified.

The method further preferably includes determining a parameterassociated with the anatomical feature S224, which functions to assess(e.g., quantify) the anatomical feature. S224 is preferably performedusing one or more computer vision/image processing techniques, whichcan, for instance, include any or all of: centerline extraction,centerline extension, a distance measurement (e.g., between two ends ofa feature, between two ends of a centerline, etc.), size measurement(e.g., length, width, thickness, volume, estimated mass, etc.),direction or orientation measurement, intensity measurement, or anysuitable measurement can be performed.

In some variations of the method, such as those implemented for asuspected vessel occlusion (e.g., LVO), a centerline length isdetermined through a centerline extension process. This can be performedthrough any or all of: binary masks, voxel thresholding, one or moretrimming steps, a three-dimensional parallel thinning algorithm, or anyother process. In an example, for instance, the centerline extensionprocess includes extending a large vessel centerline based on a set ofHU values of one or more voxels (e.g., end voxels, voxels adjacentcenterline ends of a large vessel occlusion, middle voxels, voxelshaving HU values above a predetermined threshold, voxels having HUvalues below a predetermined threshold, voxels having HU values within apredetermined range, etc.) to generate an extended centerline. Aparameter (e.g., centerline length) can then be calculated, forinstance, from the extended centerline.

In one specific example, a centerline length is determined for a vesselsegmentation, such as a vessel segmentation (e.g., probabilistic vesselsegmentation) described previously. Determining the centerline lengthcan include any or all of: conversion of image data to a mask (e.g.,binary mask), thresholding, converting the mask to a centerline (e.g.,through a three-dimensional parallel thinning algorithm), growing thecenterline (e.g., based on a predetermined set of criteria), fusing ofcenterline skeletons, preserving one or more conditions or features(e.g., topological, geometrical, etc.), pre-processing, post-processing,or any other suitable process.

In some variations, an algorithm (e.g., for determining a centerlinelength) is determined to optimize for speed. Additionally oralternatively, an algorithm can be selected to optimize for noisesensitivity or any other suitable feature.

In some variations, the process is repeated until one or more of a setof conditions are met. These conditions can include, for instance, thata parameter (e.g., distance, length, volume, area, voxel value, pixelvalue, etc.) is related in a predetermined way (e.g., within, above,below, etc.) a decision threshold, that the process has been repeatedfor a predetermined number of times (e.g., 5 times, 10 times, etc.), orany other suitable criteria.

In some variations, a trimming step is performed at the end of eachiteration to remove irrelevant features. In a specific example, forinstance, a trimming step is performed to remove (e.g., clean) shortbranches which do not represent large vessels.

The method preferably includes comparing the parameter with a thresholdS226, which functions to determine (or alternatively rule out) asuspected condition. The condition typically refers to a hypothesizedpatient condition or diagnosis (e.g., LVO, aneurysm, stroke, etc.) butcan additionally or alternatively include a severity (e.g., based on apredetermined severity scale), an urgency, or any other characteristic.

S226 is preferably performed after and in response to S224 but canadditionally or alternatively be performed at any suitable time in themethod. The threshold (e.g., threshold value) is preferably determinedbased on clinical data and/or anatomical data, such as a geometricalfeature, size (e.g., average size, aggregated size, random size, optimalsize, largest size, etc.) of an anatomical feature, intensity of afeature (e.g., contrast-filled vessel), or any other suitablecharacteristic. In some variations, the threshold is determined based onone or more training sets of data, wherein the training sets are used todevelop one or more algorithms used in the method.

S226 can optionally include determining the threshold. In somevariations, the threshold is chosen to be greater than the value (e.g.,average value, highest value, upper limit of a standard range, optimalvalue, etc.) of the corresponding anatomical feature, which can functionto increase the sensitivity of the determination of a patient condition,increase the number of false positives (e.g., when false positives havea negligible effect on a workflow), affect a specificity (e.g.,decrease) of the determination of a patient condition, or perform anyother suitable function. In one example, for instance, the thresholdagainst which a centerline length of a vessel (e.g., t-ICA plus proximalMCA-M1) is compared is chosen to be larger than the correspondinganatomical length (e.g., average total length of t-ICA and proximalMCA-M1, maximum total length of t-ICA and proximal MCA-M1, etc.).Alternatively, the threshold can be chosen to be smaller, approximatelyaverage, optimal, or otherwise comparable to an anatomical feature.

In some variations of the method, such as those implemented in patientspresenting with an LVO, a computed centerline length is determined andcompared with a threshold centerline length (e.g., larger than averagecenterline length). If the computed centerline length is less than thethreshold, an LVO is suspected. If the centerline length is greater thanthe threshold, no LVO is suspected. This can be used to determinewhether or not the patient should be transferred to a specialist, toinform a healthcare worker at a first point of care, or for any othersuitable purpose. In a specific example, a total length of a largevessel region (e.g., t-ICA and proximal MCA-M1) was determined to have aparticular value (e.g., 50 mm, 53 mm, between 50 mm and 60 mm, less than60 mm, less than 70 mm, etc.), and a threshold length was chosen to belarger (e.g., 60 mm, greater than 60 mm, etc.) than that value tooptimize for true positives.

In some variations, S226 can include performing a process during atraining step, wherein the process is used to determine on optimalthreshold. In a specific example, for instance, one or more receiveroperating characteristic (ROC) analyses are performed to investigate theperformance of an algorithm for a variety of potential thresholds,thereby determining an optimal threshold (e.g., elbow point).

In one variation, user-calibrated distance thresholds are used todetermine if the distance between the proximal and distal parts of anextracted centerline is indicative of (e.g., within a range ofthresholds) an LVO. In a specific example, user-calibrated intensitythresholds are used to determine if a partial LVO is present.

The method 200 can further include testing for a set of special cases,which can function to increase the probability that a true positive isdetected during the method. The special cases (special conditions)typically correspond to less common anatomical or clinicalconfigurations of the condition (e.g., LVO) but can additionally oralternatively correspond to a degraded image quality of a set ofinstances, or any other suitable event. In some variations of patientspresenting with an LVO, for instance, an LVO can be present even whenthe centerline length is above the predetermined threshold. This caninclude investigating one or more features of the anatomical feature andits parameters, such as an orientation of an anatomical feature (e.g.,orientation of vessel centerline), geometrical feature (e.g., width of avessel), or any other suitable feature indicative of a special case.

In one example, the method includes checking for a partial occlusion. Insuch cases, contrast can still partially fill the vessel, so acenterline extension can succeed and result in a centerline length abovethe predetermined threshold. Checking for a partial occlusion caninclude comparing the HU value of the centerline voxels to the HU valueof a set of immediately adjacent voxels. If a difference of greater thana predetermined threshold value (e.g., 200 HU) is seen between the voxelgroups, an LVO can be detected and/or indicated in future steps.

In a second example, the method includes checking for a fetal originposterior cerebral artery (PCA), which corresponds to an LVO occurringimmediately after a fetal origin PCA bifurcation, as the centerlineextension extends into the PCA instead of into the MCA. This can bedetected by inspecting an orientation of the centerline extension, andif the centerline extends posteriorly to a greater degree than itextends distally, an LVO can be detected and/or indicated in futuresteps.

Additionally or alternatively, any other special cases can be examinedin any suitable way.

4.5 Method—Determining a Treatment Option S230

The method can include determining a treatment option S230, preferablyin the event that a condition is detected (e.g., based on a comparisonwith a threshold) but can additionally or alternatively determine atreatment option when a condition is not detected, when an analysis isinconclusive, or in any suitable scenario. S230 can function to initiatethe transfer of a patient to a 2^(nd) point of care (e.g., specialistfacility), initiate the transfer of a specialist to a 1^(st) point ofcare, or initiate treatment of a patient (e.g., mechanical thrombectomy)within the 1^(st) point of care, or perform any other suitable function.In some variations, the treatment option is a 2^(nd) point of care,wherein it is determined (e.g., suggested, assigned, etc.) that thepatient should be treated at the 2^(nd) point of care. Additionally oralternatively, the treatment option can be a procedure (e.g., surgicalprocedure, mechanical thrombectomy, placement of an aneurysm coil,placement of a stent, retrieval of a thrombus, etc.), treatment (e.g.,tissue plasminogen activator (TPA), pain killer, blood thinner, etc.),recovery plan (e.g., physical therapy, speech therapy, etc.), or anyother suitable treatment.

The treatment is preferably determined based on a comparison between aparameter determined from the data packet and a threshold, but canadditionally or alternatively be determined based on additional data,such as patient information (e.g., demographic information, patienthistory, patient treatment preferences, etc.), input from one or moreindividuals (e.g., power of attorney, attending physician, emergencyphysician, etc.), or any other suitable information.

S230 is preferably at least partially performed with software operatingat the remote computing system (e.g., remote server) but canadditionally or alternatively be performed at a remote computing systemseparate from a previous remote computing system, a local computingsystem (e.g., local server, virtual machine coupled to healthcarefacility server, computing system connected to a PACS server), or at anyother location.

S230 is preferably performed after a patient condition has beendetermined during the method 200. Additionally or alternatively, S230can be performed after a patient condition has been determined in analternative workflow (e.g., at the 1^(st) point of care, at aradiologist workstation during a standard radiology workflow, in thecase of a false negative, etc.), prior to or absent the determination ofa patient condition (e.g., based on an input from a healthcare worker atthe remote computing system, when patient is admitted to 1^(st) point ofcare, etc.), multiple times throughout the method (e.g., after a firsttreatment option fails, after a first specialist is unresponsive, suchas after a threshold amount of time, such as 30 seconds, 1 minute, 2minutes, etc.), or at any other time during the method.

S230 preferably determines a treatment option with a lookup tablelocated in a database accessible at remote computing system (e.g.,cloud-computing system). Additionally or alternatively, a lookup tablecan be stored at a healthcare facility computing system (e.g., PACSserver), in storage at a user device, or at any other location.

In other variations, the treatment option can be determined by analgorithm (e.g., predictive algorithm, trained algorithm, etc.), anindividual (e.g., specialist), a decision support tool, or through anyother process or tool.

The lookup table preferably correlates a 2^(nd) point-of-care (e.g.,healthcare facility, hub, physician, specialist, neuro-interventionist,etc.), further preferably a specialist or contact (e.g., administrativeworker, emergency room physician, etc.), with a patient condition (e.g.,presence of an LVO, presence of a pathology, severity, etc.), but canadditionally or alternatively correlate any treatment option with thepatient condition. The lookup table can further additionally oralternatively correlate a treatment option with supplementaryinformation (e.g., patient history, demographic information, heuristicinformation, etc.).

The contact (e.g., healthcare provider, neuro-interventional specialist,etc.) is preferably a healthcare worker, but can additionally oralternatively be any individual associated with the treatment of thepatient and/or be associated with any healthcare facility (e.g., priorhealthcare facility of patient, current healthcare facility, recommendedhealthcare facility) related to the patient. The contact is furtherpreferably a specialist (e.g., neuro-interventional specialist,neurosurgeon, neurovascular surgeon, general surgeon, cardiacspecialist, etc.) but can additionally or alternatively include anadministrative worker associated with a specialist, multiple points ofcontact (e.g., ranked order, group, etc.), or any other suitableindividual or group of individuals. The contact is preferably associatedwith a hub facility, wherein the hub facility is determined as an optionfor second point of care, but can additionally or alternatively beassociated with a spoke facility (e.g., current facility, futurefacility option, etc.), an individual with a relation to the patient(e.g., family member, employer, friend, acquaintance, emergency contact,etc.), or any other suitable individual or entity (e.g., employer,insurance company, etc.).

The lookup table is preferably determined based on multiple types ofinformation, such as, but not limited to: location information (e.g.,location of a 1^(st) point of care, location of a 2^(nd) point of care,distance between points of care, etc.), temporal information (e.g., timeof transit between points of care, time passed since patient presentedat 1^(st) point of care, etc.), features of condition (e.g., size ofocclusion, severity of condition, etc.), patient demographics (e.g.,age, general health, history, etc.), specialist information (e.g.,schedule, on-call times, historic response time, skill level, years ofexperience, specialty procedures, historic success or procedures, etc.),healthcare facility information (e.g., current number of patients,available beds, available machines, etc.), but can additionally oralternatively be determined based on a single type of information or inany other suitable way. Information can be actual, estimated, predicted,or otherwise determined or collected.

A location can be a set of geographic coordinates (e.g., latitude andlongitude), a place name (e.g., county, city, landmark, intersection,etc.), a physical street address, distance from a given location,presence within a specified radius from a given location, a graphicaldepiction on a map, or any other suitable location expression. Thelocation can be determined based on GPS coordinates provided by adevice, triangulation between mobile phone towers and public masts(e.g., assistive GPS), Wi-Fi connection location, WHOIS performed on IPaddress or MAC address, GSM/CDMA cell IDs, location informationself-reported by a user, or determined in any other suitable manner.

In some variations, the method 200 includes transmitting information(e.g., patient condition, data determined from analysis, optimal set ofinstances, series, data packet, etc.) to the computing system associatedwith the lookup table.

4.6 Method—Preparing a Data Packet for Transfer S240

The method 200 can include preparing a data packet for transfer, whichcan function to produce a compressed data packet, partially or fullyanonymize a data packet (e.g., to comply with patient privacyguidelines, to comply with Health Insurance Portability andAccountability Act (HIPAA) regulations, to comply with General DataProtection Regulation (GDRP) protocols, etc.), minimize the time totransfer a data packet, or perform any other suitable function.Additionally or alternatively, any or all of a data packet previouslydescribed can be transferred.

The data packet is preferably transferred (e.g., once when data packetis generated, after a predetermined delay, etc.) to a contact, furtherpreferably a specialist (e.g., associated with a 2^(nd) point of care,located at the 1^(st) point of care, etc.), but can additionally oralternatively be sent to another healthcare facility worker (e.g., at1^(st) point of care, radiologist, etc.), an individual (e.g., relative,patient, etc.), a healthcare facility computing system (e.g.,workstation), a server or database (e.g., PACS server), or to any othersuitable location.

S240 preferably includes compressing a set of images (e.g., series), butcan additionally or alternatively leave the set of images uncompressed,compress a partial set of images (e.g., a subset depicting thecondition), or compress any other part of a data packet. Compressing thedata packet functions to enable the data packet to be sent to, receivedat, and viewed on a user device, such as a mobile device. Compressingthe data packet can include any or all of: removing a particular imageregion (e.g., region corresponding to air, region corresponding to hardmatter, region without contrast dye, irrelevant anatomical region,etc.), thresholding of voxel values (e.g., all values below apredetermined threshold are set to a fixed value, all values above apredetermined threshold are set to a fixed value, all values below −500HU are set to −500, all voxel values corresponding to a particularregion are set to a fixed value, all voxels corresponding to air are setto a predetermined fixed value, etc.), reducing a size of each image(e.g., scale image size by factor of 0.9, scale image size by factor of0.7, scale image size by factor of 0.5, scale image size by a factorbetween 0.1 and 0.9, reduce image size by a factor of 4, etc.), orthrough any other compression method.

In one variation, the reduction in size of a set of images can bedetermined based on one or more memory constraints of the receivingdevice (e.g., user device, mobile device, etc.).

In some variations, such as those involving a patient presenting with abrain condition (e.g., LVO), the images taken at an imaging modality(e.g., CT scanner) are compressed by determining an approximate or exactregion in each image corresponding to air (e.g., based on HU value,based on location, based on volume, etc.) and setting the air region(e.g., voxels corresponding to the air region, pixels corresponding tothe air region, etc.) to have a fixed value. Additionally oralternatively, any non-critical region (e.g., bone, unaffected region,etc.) or other region can be altered (e.g., set to a fixed value,removed, etc.) during the compression. In a specific example, forinstance, a set of voxels corresponding to air are set to all have acommon fixed value (e.g., an upper limit value, a lower limit value, avalue between 0 and 1, a predetermined value, etc.).

In some variations, S240 includes identifying an optimal visualizationto be transmitted (e.g., from a remote computing system) and received(e.g., at a user device), which functions to prepare an optimal outputfor a 2^(nd) point of care (e.g., specialist), reduce the time requiredto review the data packet, bring attention to the most relevant imagedata, or to effect any other suitable outcome.

In some variations, this is involves a reverse registration process. Ina specific example, for instance, this is done through maximum intensityprojection (MIP), where an optimal range of instances is determinedbased on which images contain the largest percentage of the segmentedanatomical region of interest in a MIP image.

Additionally or alternatively, S240 can include removing and/or altering(e.g., encrypting) metadata or any unnecessary, private, confidential,or sensitive information from the data packet. In some variations,patient information (e.g., patient-identifiable information) is removedfrom the data packet in order to comply with regulatory guidelines. Inother variations, all metadata are extracted and removed from the datapacket.

In some variations, S240 includes storing a dataset (e.g., at a remoteserver, at a local server, at a PACS server, etc.). In one example,metadata are extracted from the image data and stored separately fromimage data in a relational database. In another example, any or all ofthe data packet are stored (e.g., temporarily, permanently, etc.) to beused in one or more future analytics processes, which can function toimprove the method, better match patients with suitable treatmentoptions, or for any other suitable purpose.

In some variations, S240 includes applying a low bandwidthimplementation process, which can function to reduce the time until aspecialist receives a first piece of data or data packet (e.g., anincomplete series, incomplete study, single instance, single image,optimal image, image showing occlusion, etc.), reduce the processingrequired to inform a specialist of a potential patient condition, reducethe amount of data required to be reviewed by a specialist, reduce theamount of data being transmitted from a remote computing system to amobile device, or perform any other suitable function. The low bandwidthimplementation process can include any or all of: organizing (e.g.,chunking) data (e.g., chunking a series of images based on anatomicalregion), reordering data (e.g., reordering slices in a CT series),transmitting a portion (e.g., single image, single slice, etc.) of adata packet (e.g., series, study, set of images, etc.) to a device(e.g., user device, mobile device, healthcare facility workstation,computer, etc.), sending the rest of the data packet (e.g., only inresponse to a request, after a predetermined time has passed, once thedata packet has been fully processed, etc.), or any other process. In aspecific example, for instance, the image data (e.g., slices) receivedat a remote computing system from a scanner are chunked, reordered, anda single slice is sent to the device associated with a specialist first(e.g., prior to sending a remaining set of slices).

Additionally or alternatively, S240 can include any other suitable stepsperformed in any suitable order.

The method can additionally or alternatively include any other suitablesub-steps for preparing the data packet.

4.7 Method—Transmitting Information to a Device Associated with the2^(nd) Point of Care S250

Transmitting information to a device associated with the 2^(nd) point ofcare (e.g., specialist, contact, etc.) S250 (e.g., as shown in FIG. 6)functions to initiate a pull from a 1^(st) point of care to a 2^(nd)point of care, which can decrease time to care, improve quality of care(e.g., better match between patient condition and specialist), or haveany other suitable outcome. Preferably, the 2^(nd) point of care is ahub facility (e.g., specialist facility, interventional center,comprehensive stroke center, etc.). In some variations, the 1^(st) pointof care (e.g., healthcare facility at which patient initially presents)also functions as the 2^(nd) point of care, such as when a suitablespecialist is associated with the 1^(st) point of care, the 1^(st) pointof care is a hub (e.g., specialist facility, interventional center,comprehensive stroke center, etc.), it is not advised to transfer thepatient (e.g., condition has high severity), or for any other reason.

S250 is preferably performed after (e.g., in response to) a 2^(nd) pointof care is determined, but can additionally or alternatively beperformed after a data packet (e.g., compressed data packet, encrypteddata packet, etc.) has been determined, multiple times throughout themethod (e.g., to multiple recipients, with multiple data packets, withupdated information, after a predetermined amount of time has passedsince a notification has been sent to a first choice specialist, etc.),or at any other time during the method 200.

The device is preferably a user device, further preferably a mobiledevice. Examples of the user device include a tablet, smartphone, mobilephone, laptop, watch, wearable device (e.g., glasses), or any othersuitable user device. The user device can include power storage (e.g., abattery), processing systems (e.g., CPU, GPU, memory, etc.), useroutputs (e.g., display, speaker, vibration mechanism, etc.), user inputs(e.g., a keyboard, touchscreen, microphone, etc.), a location system(e.g., a GPS system), sensors (e.g., optical sensors, such as lightsensors and cameras, orientation sensors, such as accelerometers,gyroscopes, and altimeters, audio sensors, such as microphones, etc.),data communication system (e.g., a WiFi module, BLE, cellular module,etc.), or any other suitable component.

The device is preferably associated (e.g., owned by, belonging to,accessible by, etc.) a specialist or other individual associated withthe 2^(nd) point of care, but can additionally or alternatively beassociated with an individual or computing system at the 1^(st) point ofcare, the patient, or any other suitable individual or system.

In one variation, the device is a personal mobile phone of a specialist.In another variation, the device is a workstation at a healthcarefacility (e.g., first point of care, second point of care, etc.).

The information preferably includes a data packet, further preferablythe data packet prepared in S240. Additionally or alternatively, theinformation can include a subset of a data packet, the original datapacket, any other image data set, or any other suitable data. Theinformation further preferably includes a notification, wherein thenotification prompts the individual to review the data packet at thedevice (e.g., a message reciting “urgent: please review!”). Additionallyor alternatively, the notification can prompt the individual to reviewdata (e.g., original data packet, uncompressed images, etc.) at aseparate device, such as a workstation in a healthcare facility, a PACSserver, or any other location. Further additionally or alternatively,the notification can include any suitable information, such as, but notlimited to: instructions (e.g., for treating patient, directions forreaching a healthcare facility), contact information (e.g., foremergency physician at first point of care, administrative assistant,etc.), patient information (e.g., patient history), or any othersuitable information.

The notification preferably includes an SMS text message but canadditionally or alternatively include an email message, audio message(e.g., recording sent to mobile phone), push notification, phone call,or any other suitable notification.

The information is preferably sent to the device through a clientapplication executing on the user device but can additionally oralternatively be sent through a messaging platform, web browser, orother platform.

In some variations, a notification is sent which prompts the individualto provide an input, wherein the input can indicate that the individualwill view, has viewed, or is in the process of viewing the information(e.g., image data), sees the presence of a condition (e.g., truepositive, serious condition, time-sensitive condition, etc.), does notsee the presence of a condition (e.g., false positive, seriouscondition, time-sensitive condition, etc.), has accepted treatment ofthe patient (e.g., swipes right, swipes up, clicks a check mark, etc.),has denied treatment of the patient (e.g., swipes left, swipes down,clicks an ‘x’, etc.), wants to communicate with another individual(e.g., healthcare worker at 1^(st) point of care), such as through amessaging platform (e.g., native to the device, enabled by the clientapplication, etc.), or any other input. In some variations, one or moreadditional notifications are provided to the individual (e.g., based onthe contents of the input), which can be determined by a lookup table,operator, individual, decision engine, or other tool. In one example,for instance, if the individual indicates that the condition is a truepositive, information related to the transfer of the patient (e.g.,estimated time of arrival, directions to the location of the patient,etc.) can be provided (e.g., in a transfer request, wherein patienttransfer to a specified location, such as the 2^(nd) point of care, canbe initiated upon transfer request receipt). In some variants, the data(e.g., images) are displayed on the user device (e.g., mobile device,workstation) in response to user interaction with the notification(e.g., in response to input receipt). However, the input can trigger anysuitable action or be otherwise used.

Additionally or alternatively, an input can automatically be receivedfrom the client application, such as a read receipt when the individualhas opened the data packet, viewed the notification, or interacted withthe client application in any other suitable way. In one example, if aread receipt is not received (e.g., at the remote computing system) fromthe device within a predetermined amount of time (e.g., 10 seconds), asecond notification and/or data packet (e.g., compressed set of images)are sent to a second individual (e.g., second choice specialist based ona lookup table).

In some variations, various outputs can be sent from the clientapplication (e.g., at the user device) to one or more recipients (e.g.,to a second user device, client application on a work station, on acomputing system, etc.), such as recipients associated with a firstpoint of care (e.g., radiologists, emergency physicians, etc.). Theoutputs can be determined based on the inputs received at the clientapplication associated with the individual (e.g., acceptance of case,verification of true positive, etc.), based on a lookup table, orotherwise determined. The outputs preferably do not alter the standardradiology workflow (e.g., are not shared with radiologists; radiologistsare not notified), which functions to ensure that the method 200 is atrue parallel process, and that the standard radiology workflow resultsin an independent assessment of the patient, but can additionally oralternatively cut short a workflow, bring a specialist in on the patientcase earlier than normal, or affect any other process in a healthcarefacility.

4.8 Method—Aggregating Data S260

The method 200 can optionally include any number of sub-steps involvingthe aggregation of data involved in and/or generated during the method200, which can function to improve future iterations of the method 200(e.g., better match patients with a specialist, decrease time to treat apatient, increase sensitivity, increase specificity, etc.). Theaggregated data is preferably used in one or more analytics steps (e.g.,to refine a treatment option, make a recommendation for a drug orprocedure, etc.), but can additionally or alternatively be used for anyother suitable purpose.

In some variations, for instance the outcomes of the patients examinedduring the method 200 are recorded and correlated with theircorresponding data packets, which can be used to assess the success ofthe particular treatment options chosen and better inform treatmentoptions in future cases.

4.9 Method—Variations

In one variation, the method functions to augment a standard radiologyworkflow operating in parallel with the method, which can include any orall of: at a remote computing system (e.g., remote from the first pointof care), receiving a set of images (e.g., of a brain of the patient),wherein the set of images is concurrently sent to the standard radiologyworkflow operating in parallel with the method and automaticallydetecting a condition (e.g., potential large vessel occlusion) from theset of images. Upon condition detection, the method can include any orall of, automatically: determining a second specialist from the standardradiology workflow, wherein the specialist is associated with a secondpoint of care; notifying the second specialist on a mobile deviceassociated with the second specialist before the radiologist notifiesthe first specialist; and displaying a compressed version of the set ofimages on the mobile device.

In a specific example, the method includes, at a remote computingsystem, receiving a set of Digital Imaging and Communications inMedicine (DICOM) brain images associated with the patient, wherein theset of DICOM brain images is concurrently sent to a standard radiologyworkflow operating in parallel with the method. In the standardradiology workflow, the radiologist analyzes the set of DICOM brainimages and notifies a specialist based on a visual assessment of the setof DICOM brain images at the workstation, wherein the standard radiologyworkflow takes a first amount of time. The method can then includedetecting a potential cerebral artery occlusion from the set of DICOMbrain images, which includes any or all of: identifying a large vesselregion from the set of DICOM brain images; extracting a centerline fromthe large vessel region; determining a centerline length of the largevessel region based on the centerline; comparing the centerline lengthwith a predetermined threshold; and detecting the potential cerebralartery occlusion when the centerline length is less than thepredetermined threshold. Upon potential cerebral artery occlusiondetection, the method can include, automatically: determining thespecialist from the standard radiology workflow, wherein the specialistis associated with a second point of care; notifying the specialist on amobile device associated with the specialist, wherein the specialist isnotified in a second amount of time shorter than the first amount oftime, wherein the radiologist is not automatically notified uponpotential cerebral artery occlusion detection; displaying a compressedversion of the set of DICOM brain images on the mobile device; anddisplaying a high-resolution version of the set of DICOM brain images ona workstation associated with the specialist. Additionally oralternatively, the method can include any other suitable process.

In another variation, the method functions to determine a specialist(e.g., independently of the performance of a radiology workflow, inparallel with a radiologist workflow, bypassing a radiologist workflow,etc.), where the method includes: receiving a data packet comprising aset of images (e.g., CT images of a brain of the patient) sampled at thefirst point of care, where the data packet is concurrently sent to thestandard radiology workflow; determining an anatomical feature (e.g.,large vessel region) from the set of images; extracting a feature (e.g.,large vessel centerline) from the region; determining a parameter (e.g.,calculating a centerline length) of the feature; and comparing theparameter (e.g., centerline length) with a predetermined threshold. Inone example, the method can then include detecting a large vesselocclusion when the centerline length is less than the predeterminedthreshold. In response to the detection of a condition (e.g., largevessel occlusion detection), the method can include any or all of:presenting a notification on a mobile device associated with aspecialist from the standard radiology workflow, the specialistassociated with a second point of care, displaying a compressed versionof the set of images on the mobile device in response to interactionwith the notification, or any other suitable process.

In a specific example, the method includes: receiving, at a remotecomputing system, a data packet from the first point of care, the datapacket including a set of computed tomography (CT) images and a set ofmetadata associated with the set of CT images; processing the datapacket at the remote computing system, which can include any or all of:organizing the set of CT images into a series based on the metadata,identifying soft matter voxels from the series based on a soft mattermask, the soft matter mask including a predetermined Hounsfield Unit(HU) threshold, registering the soft matter voxels to a set of referenceCT images, thereby determining a registered set of voxels, segmenting(e.g., with a feed-forward deep convolutional network) a large vesselregion in the registered set of voxels, extracting a centerline of thesegmented large vessel region, and determining a length of the segmentedlarge vessel region based on the centerline. With the centerline length,the method can then include: comparing the centerline length with apredetermined threshold, wherein the predetermined threshold is greaterthan a corresponding anatomical length. When the centerline length isless than the predetermined threshold, a specialist can be determinedbased on a lookup table. Then, a notification and a second data packetcomprising a set of compressed images can be transmitted to a userdevice associated with the specialist.

Additionally or alternatively, the method can include any other stepsperformed in any suitable order.

Although omitted for conciseness, the preferred embodiments includeevery combination and permutation of the various system components andthe various method processes, wherein the method processes can beperformed in any suitable order, sequentially or concurrently.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the preferred embodiments of the invention withoutdeparting from the scope of this invention defined in the followingclaims.

We claim:
 1. A method for computer-aided triage, the method comprising,at a remote computing system remote from a first point of care:receiving, at the remote computing system, a set of Digital Imaging andCommunications in Medicine (DICOM) images associated with the patientand taken at the first point of care, wherein the set of DICOM images isconcurrently sent to a standard radiology workflow operating in parallelwith the method, wherein, in the standard radiology workflow, aradiologist analyzes the set of DICOM images at the first point of careand notifies a specialist based on a visual assessment of the set ofDICOM images at a workstation, wherein the standard radiology workflowtakes a first amount of time; at the remote computing system,automatically detecting a potential pathology from the set of DICOMimages based on an automated processing of the set of DICOM images; upondetecting the potential pathology from the set of DICOM images,automatically: determining, at the remote computing system, thespecialist from the standard radiology workflow, wherein the specialistis associated with a second point of care; notifying the specialist on amobile device associated with the specialist, wherein the specialist isnotified in a second amount of time shorter than the first amount oftime, wherein the radiologist is not automatically notified uponpotential cerebral artery occlusion detection; displaying a compressedversion of the set of DICOM brain images on the mobile device; anddisplaying a high-resolution version of the set of DICOM brain images ona workstation associated with the specialist; receiving an input fromthe specialist, wherein the input initiates a transfer request;receiving the transfer request at a second workstation located at thefirst point of care; initiating a transfer of the patient from the firstpoint of care to the second point of care upon transfer request receipt.2. The method of claim 1, wherein the automated processing comprises:segmenting the set of DICOM images to isolate a region associated withthe potential pathology; calculating a parameter associated with theregion; comparing the parameter with a predetermined threshold; anddetecting the potential pathology based on the comparison.
 3. The methodof claim 2, wherein the set of DICOM images comprises a set of DICOMbrain images, and wherein: the region comprises a vessel region of thebrain; the parameter quantifies an occlusion in the vessel region; andthe potential pathology comprises a vessel occlusion.
 4. The method ofclaim 1, wherein the second amount of time is less than 8 minutes. 5.The method of claim 1, wherein processing the set of DICOM images isinitiated after a predetermined time period has passed after receivingat least a first of the set of DICOM images at the remote computingsystem.
 6. The method of claim 1, wherein the automated processing isconfigured to detect the potential pathology with a specificity below apredetermined threshold.
 7. The method of claim 1, wherein notifying thespecialist comprises presenting a notification on the mobile device, themethod further comprising, prior to displaying the compressed version ofthe set of DICOM images: monitoring for an input associated with thenotification; displaying the compressed version of the set of images onthe mobile device after receipt of the input; and when the input is notreceived within a predetermined time threshold, determining a secondspecialist and presenting the notification on a second mobile deviceassociated with the second specialist.
 8. The method of claim 7, whereinthe radiologist is not automatically notified upon detecting thepotential pathology, wherein the radiologist in the standard radiologyworkflow notifies the specialist at a second time after notifying thesecond specialist on a mobile device.
 9. The method of claim 7: whereina mobile device application executing on the mobile device presents thenotification and displays the compressed version of the set of images,wherein the specialist is logged into the mobile device applicationthrough a specialist account; and wherein a workstation applicationexecuting on the workstation associated with the specialist displays thehigh-resolution version of the set of images, wherein the specialist islogged into the workstation application through the specialist account.10. A method for computer-aided triage, the method comprising, at aremote computing system remote from a first point of care: receiving, atthe remote computing system, a set of Digital Imaging and Communicationsin Medicine (DICOM) images associated with the patient and taken at thefirst point of care, wherein the set of DICOM images is concurrentlysent to a standard radiology workflow operating in parallel with themethod, wherein, in the standard radiology workflow, a radiologistanalyzes the set of DICOM images at the first point of care and notifiesa specialist based on a visual assessment of the set of DICOM images ata workstation, wherein the standard radiology workflow takes a firstamount of time; at the remote computing system, automatically detectinga potential pathology from the set of DICOM images based on an automatedprocessing of the set of DICOM images; upon detecting the potentialpathology from the set of DICOM images, automatically: determining, atthe remote computing system, the specialist from the standard radiologyworkflow, wherein the specialist is associated with a second point ofcare; notifying the specialist on a mobile device associated with thespecialist, wherein the specialist is notified in a second amount oftime shorter than the first amount of time, wherein the radiologist isnot automatically notified upon potential pathology detection;displaying a compressed version of the set of DICOM brain images on themobile device; and displaying a high-resolution version of the set ofDICOM brain images on a workstation associated with the specialist; andreceiving an input from the specialist, wherein the input initiates atransfer of the patient.
 11. The method of claim 10, further comprisingreceiving a transfer request at a second workstation located at thefirst point of care.
 12. The method of claim 11, further comprisinginitiating a transfer of the patient from the first point of care to thesecond point of care upon transfer request receipt.
 13. The method ofclaim 10, wherein the automated processing comprises: segmenting the setof DICOM images to isolate a region associated with the potentialpathology; calculating a parameter associated with the region; comparingthe parameter with a predetermined threshold; and detecting thepotential pathology based on the comparison.
 14. The method of claim 13,wherein the set of DICOM images comprises a set of DICOM brain images,and wherein: the region comprises a vessel region of the brain; theparameter quantifies an occlusion in the vessel region; and thepotential pathology comprises a vessel occlusion.
 15. The method ofclaim 10, wherein the second amount of time is less than 8 minutes. 16.The method of claim 10, wherein processing the set of DICOM images isinitiated after a predetermined time period has passed after receivingat least a first of the set of DICOM images at the remote computingsystem.
 17. The method of claim 10, wherein the automated processing isconfigured to detect the potential pathology with a specificity below apredetermined threshold.
 18. The method of claim 10, wherein notifyingthe specialist comprises presenting a notification on the mobile device,the method further comprising, prior to displaying the compressedversion of the set of DICOM images: monitoring for an input associatedwith the notification; displaying the compressed version of the set ofimages on the mobile device after receipt of the input; and when theinput is not received within a predetermined time threshold, determininga second specialist and presenting the notification on a second mobiledevice associated with the second specialist.
 19. The method of claim18, wherein the radiologist is not automatically notified upon detectingthe potential pathology, wherein the radiologist in the standardradiology workflow notifies the specialist at a second time afternotifying the second specialist on a mobile device.
 20. The method ofclaim 10: wherein a mobile device application executing on the mobiledevice presents the notification and displays the compressed version ofthe set of images, wherein the specialist is logged into the mobiledevice application through a specialist account; and wherein aworkstation application executing on the workstation associated with thespecialist displays the high-resolution version of the set of images,wherein the specialist is logged into the workstation applicationthrough the specialist account.